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December 08, 2009

FT: Organic mechanics

Economists talking about "efficient" or "perfect" markets remind Lord May of ecologists talking about "the balance of nature" 40 years ago, when ecosystems with a rich web of interactions were thought to be the most stable. Subsequent analysis has shown the opposite to be the case: the most robust systems can be decoupled into discrete components without collapsing.

Mathematical biology also helps to explain in retrospect why hedge funds, the institutions once thought to be at greatest risk of financial collapse, have survived the crisis in a healthy state. Compared with banking, the hedge fund sector is populated with relatively small, specialised players - the robust structure of a diverse ecosystem.

W hat do you call a financier in search of the iron laws of human behaviour? Answer: someone with a bad case of "physics envy".

That
is the peculiar psychological disorder diagnosed by Andrew Lo, a
professor of financial engineering, as afflicting bankers and
economists. Symptoms include a desperate search for the predictive
certainty that comes from the hard sciences.

At least since the
18th century, economists have been borrowing from physics, redeploying
everything from thermodynamics and the "conservation of energy"
principle to the understanding of macroeconomics and the generation of
fancy derivatives. The global financial crisis has, however, seen
financiers cast their scientific net further as they try to understand
what went wrong and how to make the banking system more stable in
future. As a result, they are developing "biology envy".

Bankers
and financial economists are working with mathematical biologists to
learn lessons about resilience from natural ecosystems - from fisheries
to forests - and from the spread of disease. The exercise is certainly
of more than academic interest. Andrew Haldane, executive director for
financial stability at the Bank of England, says the regulatory
structure for banking may be shaped by studies now in progress that
treat global finance as a "complex adaptive system" like a living
ecosystem.

The outcome could determine whether the system is
robust enough to survive another financial storm without casualties on
the scale of Lehman Brothers and without the need for governments to
spend thousands of billions of taxpayer dollars to prevent a collapse.

Some
policy conclusions are already clear. One is that the banking system
has become at the same time too complex and too homogeneous. The
problem is that over the past 20 years or so almost all the big
globally active banks diversified their holdings and risk, moving into
increasingly complex (and opaque) financial instruments. Unfortunately
for the stability of the whole system, banks all diversified their
business lines in a similar way and, in the process, became
inextricably interdependent.

"From an individual firm's
perspective, these strategies looked like sens-ible attempts to purge
risk through diversification: more eggs are being placed in the
basket," says Mr Haldane. "Viewed across the system as a whole,
however, it is clear now that these strategies generated the opposite
result: the greater the number of eggs, the greater the fragility of
the basket - and the greater the probability of bad eggs."

That
is what a mathematical ecologist would have predicted if he or she had
known what was going on in the world of finance. The tropical
rainforest, for example, has so many interdependent species that it is
more vulnerable to an external shock than the simpler ecological
diversity of savannahs and grasslands.

Mathematical biology also
helps to explain in retrospect why hedge funds, the institutions once
thought to be at greatest risk of financial collapse, have survived the
crisis in a healthy state. Compared with banking, the hedge fund sector
is populated with relatively small, specialised players - the robust
structure of a diverse ecosystem.

One distinguished mathematical
biologist who is delving deep into the financial ecosystem is Lord
Robert May, zoology professor at Oxford university and former president
of Britain's Royal Society. The financial theorists have a lot of
ground to make up, he says: "The more I hear about financial economics,
the more I am struck by its similarity to ecology in the 1960s."

Economists
talking about "efficient" or "perfect" markets remind Lord May of
ecologists talking about "the balance of nature" 40 years ago, when
ecosystems with a rich web of interactions were thought to be the most
stable. Subsequent analysis has shown the opposite to be the case: the
most robust systems can be decoupled into discrete components without
collapsing.

Some were becoming concerned about systemic risk
before the financial crisis erupted. The Bank of England started
experimenting about five years ago with computer models of the banking
system as an ecological network. The US National Academy of Sciences
and the Federal Reserve Bank of New York launched a joint study in 2006
that brought together 100 experts to explore parallels between systemic
risk in the financial sector and various fields of science and
technology, from ecology to engineering. But the financial storm had
set in by the time its conclusions were published.

Fisheries
management has interesting parallels with financial regulation, says
Lord May. For the past 50 years fish stocks have been managed on a
species-by-species basis that aims to maximise the "sustainable yield"
of individual fish such as cod or herring - an approach analogous to
regulatory risk analysis that focuses on individual banks. But with the
collapse of some important fishing grounds, marine scientists are
coming to recognise that what really matters is the wider ecosystem and
environmental context. You cannot protect cod, for example, without
considering the sand eels, whiting, haddock, squid and other species on
which cod feed.

Medical epidemiology is another fruitful
borrowing ground for financial analysis. Just as epidemiologists trying
to stem an outbreak of disease want to focus on identifying and
vaccinating the most dangerous "super-spreaders" of infection,
regulators need to control the damaging consequences for the whole
banking network of the failure of large, interconnected institutions.

International
banking rules such as Basel II have had the perverse effect of imposing
the greatest capital restrictions on the smaller and less diversified
banks that posed the least risk to the system, while the large
"super-spreader" institutions were given more leeway. Borrowing an
analogy from sexually transmitted disease, Mr Haldane says: "Basel
vaccinated the naturally immune at the expense of the contagious; the
celibate were inoculated, the promiscuous intoxicated."

Further
insights are emerging from a collaboration between David Rand at
Harvard university's programme for evolutionary dynamics and Nicholas
Beale, who runs Sciteb, a London consultancy. "The fundamental
requirement for the regulator is to ensure that the banks do not all
diversify in the same way but rather we have 'diverse
diversification'," Mr Beale says.

Their approach, rooted in
mathematical models from evolutionary biology, "gives the real prospect
of regulators being able to prevent dangerous 'herding', based on some
simple, deep and new properties of financial networks", he adds. A key
element of the new system would be to provide banks with a "systemic
risk rating" for each asset class, in a way that would induce them to
diversify in different directions.

T here is scope, too, for
borrowing from epidemiology when it comes to gathering, analysing and
communicating data. The World Health Organisation is constantly
monitoring the globe for early signs of an epidemic of infectious
disease - and if one breaks out, as Sars did in 2003 or swine flu this
year, it provides vital information to governments, medical
professionals and the general public. The banking world could do with
an equivalent of the WHO, says Mr Haldane.

At the Massachusetts
Institute of Technology, Prof Lo himself proposes that the US should
set up a capital markets safety board to manage systemic risk, modelled
on America's National Transportation Safety Board.

While the
analysis of ecosystems is the latest attempt to harness mathematical
biology to finance, such systems analysis is not confined to biology.
Experts have also seen useful lessons for banking stability in the way
engineers protect electric power grids from collapse. Some others fancy
a move back to physics, on a more sophisticated level. Theories that
have dominated finance are drawn from research that took place in
academia many years earlier - and was often reworked at around the same
time as the concepts were permeating finance.

The crude forms of
the "efficient market hypothesis" developed in the 1970s began to
refashion the banking world in the 1990s, by which time the academic
branch of economics was moving towards more subtle forms of behavioural
finance. Similarly, the forms of classical physics that have driven
financial engineering have long been superseded by more complex
theories, such as refinements of relativity and quantum theory.

If
biology does not do the trick, some of the more subtle and advanced
concepts in physics might yet be able to shed light on economics. Or so
some of the disenchanted quantitative analysts hope.

Changing the hypothesis: why 'adaptive' trumps 'efficient'

Economists
have always been keen to borrow principles from the hard sciences. In
the 19th century Léon Walras and William Stanley Jevons both started
their work with a view to importing the insights of physics into the
economic sphere. Irving Fisher, the great neoclassical economist whose
1930s work has been rediscovered during this crisis, even wrote his
doctoral thesis at the turn of the 20th century under the supervision
of a physicist.

This tendency was given renewed impetus in the
mid-20th century by Paul Samuelson's application to economics of
mathematical principles derived from thermodynamics. The development of
computers able rapidly to analyse data made the development of
mathematically elegant economic models particularly desirable, driving
the acceptance of concepts such as American economist Eugene Fama's
efficient market hypothesis.

Most of the "quants" - financial
mathematicians - who used such concepts to build financial models
always knew that this project had serious flaws. Emanuel Derman, for
example, a physicist turned financier who formerly worked at Goldman
Sachs, is credited with playing a central role in the development of
models in relationship to derivatives. Yet more than a decade ago, he
was warning Goldman Sachs clients of the limitations of derivatives
models - he compared their relationship to reality to that between a
child's toy car and an actual automobile.

Mr Derman remains, to
say the least, wary of the idea that efficient markets hypothesis can
provide a "complete" guide to finance. "Unfortunately, absolute value
theories don't work very well in economics," he wrote recently. "It's
difficult or well-nigh impossible to systematically predict what's
going to happen. You may think you know you're in a bubble, but you
still can't tell whether things are going up or down the next day."

Such
scepticism has not often been expressed quite so frankly. On the
contrary, some quants have furtively revelled in the power that their
apparently elite knowledge gave them. "The dirty secret of banking is
that lots of bankers have always felt a bit insecure because they did
not really understand how this stuff worked - so those who understood
it were in a strong position," observes one banker.

However now
that the crisis has exposed their shortcomings, the EMH and the entire
model-based approach to finance are facing a radical rethink. A growing
chorus of financiers, quants and economists argues that it is wrong to
apply simplistic assumptions that underpin the physics-like models to
people, since - unlike atoms, say - they can learn from each other and
change in response to events. Changes may not happen in a neat, linear
fashion.

Donald MacKenzie of Edinburgh university says the real
problem with models is that bankers tend to view them as "cameras" that
capture how the world works, like the camera that might photograph a
physics experiment. Instead, he argues, they should be viewed as
"engines" - since the presence of a model tends to change and drive
market behaviour in a way that makes it impossible to assume that the
past can predict the future.

Nevertheless, no alternative
intellectual model - or source of inspiration - has emerged to offer a
truly coherent alternative. George Soros, the former hedge fund
manager, for example, argues that market participants need to embrace
the idea of "reflexivity", to recognise that markets change in response
to participants, and to accept that models are an "engine, not camera".
However, turning this reflexivity theory into any investment manual or
strategy has proved difficult.

Hence the move to look at branches
of science beyond physics - and at biology in particular. Professor
Andrew Lo of MIT has developed the adaptive market hypothesis,
attempting to introduce the principles of evolution - competition,
adaptation and natural selection - to his financial models.

Prof
Lo believes that some of the features of human behaviour - such as loss
aversion, overconfidence, overreaction and other behavioural biases -
that are underappreciated by simpler models are, in fact, rational.
These aspects of human behaviour, while not conforming to the
caricature of homo economicus , may be optimal strategies for human behaviour that have been honed by millennia of evolutionary pressure.

Indeed,
he takes this evolutionary process seriously: he is fond of pointing
out to his audiences that they have both "mammalian" and "reptilian"
brains that can be employed at different moments. Prof Lo believes that
prices reflect not just information in the market place, but also
deep-seated and slowly evolved human biases.

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